Reflect on this weekend’s Residency experience.

Share 3 positive outcomes or instances of new information you acquired from any source, including your own research for your project.

In discussion with your peers feel free to ask questions based on last week’s video introductions, the intros shared during residency, and the executive overview reports from Sunday.

Running Head: EXECUTIVE OVERVIEW 1

EXECUTIVE OVERVIEW 1

Executive Overview

Student’s Name

Institutional Affiliations

Yore Blends (YB) is a fictitious online business devoted to the selling of monthly fee-based traditional spice mixes and other complementary items. In the future, they plan to expand through acquisitions, which require a large consumer base and stable revenue. In the short term, they are worried about the consumer churn (the number of consumers who have decided to buy their goods and services). The key issue of the business is created by the operation of the client churn. Customers have quit buying the goods and services provided by Yore Blends. On the basis of the issues of the organization, a theory was created to better explain the causes and the causes for the issues found. On the basis of the theory, Yore Blends is faced with a possible client turnover due to poor customer relations. It is also likely that their customers may not receive value for the goods or services they have bought (Hazen, 2014). A further theory is that they are unable to interact efficiently with their customers about product changes. Finally, it was believed that the issue was explained by natural factors.

In order to better understand and fix this problem, it was important to use correct data for accurate data. Relevant data points were thus always had to perform churn statistics. The corporation’s data was obtained from the billing system, the company’s business range, customer feedback and customer profile centered on customer service. This information allowed the development of its most effective analytical methods to solve the issue. A model of logistic regression was used to create a predictive model. The findings of the study were then confirmed using a regular spontaneous sub-sampling methodology.

Results findings indicated that the organization would consciously use all networks: mobile, e-mail, website, live conversation, and social media (Salloum, 2016). The important input on in what way well they represent their clients is just a mobile call, an e-mail or a review. Another way to avoid turnover is to constantly involve consumers with the product. Comprehending the possible effects of client turnover allows the business to implement approaches to avoid the loss of clients and to maximize product losses.

References

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.

Salloum, S., Dautov, R., Chen, X., Peng, P. X., & Huang, J. Z. (2016). Big data analytics on Apache Spark. International Journal of Data Science and Analytics, 1(3-4), 145-164.

ANALYTICS PLAN TO REDUCE CUSTOMER CHURN AT YORE BLENDS

Himabindu Aratikatla

University of the Cumberland’s

March 22, 2020

Introduction
Yore Blends (YB) is a fictional online company dedicated to selling subscription-based traditional spice blends coupled with additional complementary products.
Yore Blends (YB) aspire to growing through mergers and acquisitions.
To do this, they need a strong customer base and steady revenue.
Yore Blends is concerned with the rate of customer churn.  

Company’s Problem
Yore Blends has been in existence for years.
Nonetheless, the company is considering to expand through mergers and acquisition.
However, they are experiencing customer churn.
A considerable percentage of its clients don’t purchase their goods anymore.
As a result, the company needs to reduce customer attrition by at least 16%.

Causes for Customer Churn
Poor customer care service:
The company minimized rather than maximizing client cost
Bad onboarding:
Yore Blends clients failed to get value for the purchased products.
Clients might have lost interest in the company’s products.

Many companies think of customer service as a cost to be minimized, rather than an investment to be maximized. Here’s the issue with that: if you think of support as a cost center, then it will be. That is, if you don’t prioritize support and work to deliver excellent service to your customers, then it’s only going to cost you money…and customers. A disproportionate amount of your customer churn will take place between (1) and (2).
That’s where customers abandon your product because they get lost, don’t understand something, don’t get value from the product, or simply lose interest.
Bad onboarding – the process by which you help a customer go from (1) to (2) – can crush your retention rate, and undo all of that hard work you did to get your customers to convert in the first place.

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Causes for Customer Churn (Cont.)
Limited customer success:
Lack of updates regarding new products
Extended absence of the company-client communication
Natural Causes:

Customers may have grown out of the products.
May have resulted due to Vendor switches might

While onboarding gets your customer to their initial success, your job isn’t done there. Hundreds of variables – including changing needs, confusion about new features and product updates, extended absences from the product and competitor marketing – could lead your customers away. If your customers stop hearing from you, and you stop helping them get value from your product throughout their entire lifecycle, then you risk making that lifecycle much, much shorter. Furthermore, Not every customer that abandons you does so because you failed. Sometimes, customers go out of business. Sometimes, operational or staff changes lead to vendor switches. Sometimes, they simply outgrow your product or service. (Salloum, 2016)

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REASONS TO ANALYZE CUSTOMER CHURN
The company will be in a position to understand client behavior.
The firm can predict clients with the probability of churning.
Contributes to customer retention programs.
Helps in the identification of the causes of client churn.

Customer churn analysis refers to the customer attrition rate in a company. This analysis helps SaaS companies identify the cause of the churn and implement effective strategies for retention. Gainsight understands the negative impact that churn rate can have on company profits. Named as the “2014 cool vendor for CRM sales” (by Gartner), Gainsight’s customer intelligence and retention process automation technology:
Gathers available customer behavior, transactions, demographics data and usage pattern
Converts structured and unstructured data/information into meaningful insights
Utilizes these insights to predict customers who are likely to churn
Identifies the causes for churn and works to resolve those issues
Engages with customers to foster relationships (Salloum, 2016)
Implements effective programs for customer retention

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Effects of Customer Churn
In the event that the company experiences customer churn, it will be affected in the following ways;
It hurts the company’s valuations.
Gives advantages to the exiting competitors.
Customer churn is an indication of bigger problems for the company.
The eventualities of client churn are long-reaching for the company.

The Aftermath of Churn is Long-Reaching
Some businesses think, “If a customer stays long enough to pay for the cost of acquiring them, then we’ve operated at a net profit.” This ignores all the value you’ve lost from future opportunities with them. You’ve now lost the chance to upsell them with other products or services in your portfolio. You’ve reduced your total addressable market because those customers have been removed from it. What’s more, your brand is now likely tarnished in the minds of your spurned customer.
Churn Helps Your Competitors
Churned clients tend to be vocal about why they’ve left you. Negative customer reviews are a gift to your competitors. As positive customer references can often make or break a deal, negative references act as powerful ammunition for your competitors to position against your product. And your competitors have no qualms about using them again and again with new prospects (Hazen, 2014). Those churned customers just may come back to prevent you from closing your next deal and hitting your revenue goals.
High Customer Churn May Indicate Bigger Problems
Just like happy marriages don’t end in divorce, happy customers rarely leave. High churn indicates that something is failing in your customer relationships. Has your product or service failed to deliver on client expectations? Does your customer onboarding process need to be fixed? Are you devoting enough attention to your clients? Even if a high churn rate is from factors beyond your control, it reflects poorly on your company and your product.
Churn Hurts Your Valuation
Churn rate is a critical factor in how investors view your company. VC firms look at customer churn to determine if your product has legs on the market, and retention rate is essential in Software-as-a-Service (SaaS) public valuations. Smart SaaS investors use Customer Lifetime Value (LTV) as a metric to predict how much profit you will make and analyze the health of a company. High churn rates reduce LTV and can make investors doubt the strength of your business. So, if you’re churning and burning your customers, you’re likely making your investors pretty unhappy.

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How Your Yore Blends can Identify Customer Churn Possibility
Decreasing number of support ticket
Negative reviews and increasing complaints
Reducing website activity

Not having many support tickets might sound like a good thing, but it can suggest that customers are unengaged and have not fully adopted the company’s product. You could assume they’re a product genius who never needs help, but it’s more likely they haven’t invested in the product and aren’t taking the time to figure it out. Your product might be on the budget chopping block next year.
Direct feedback, including negative reviews and social media complaints, is one of the best indicators of company’s customers’ risk status. It’s clear that these customers have a problem with the product, service, or organization, and just because they didn’t explicitly mention leaving doesn’t mean they aren’t thinking about it.
By using a tool like web tracking, the HR may be able to see when customers view cancellation or downgrade pages – a clear sign they’re interested in moving on from the company’s product. (Hazen, 2014)
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Churn Analysis in Yore Blends
Yore Blends should carry out churn analysis to avoid loosing its clients.
This analysis will help the company to;
Measure churn rate
Monitor churn rate
Reduce churn rate

To prevent losing customers through customer attrition, companies turn to churn analytics. This type of analytics helps them measure, monitor and reduce the churn rate. The need for customer churn analytics is one of the reasons clients turn to BI implementation services. In this article, our BI experts summarize the main benefits customer churn analysis can bring and explain how to conduct it. (Hazen, 2014)
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Appropriate Data Source for Churn Analytics
Company’s billing System
Portfolio of the company’s products
Customer profiling based on customer care services
Client’s sentiments on the company’s blogs and posts

Proposed Customer Calculations fo for Yore Blends

Calculating customer (=subscription) churn alone is not informative enough for this company, as the percentage of all customers who choose to cease the relationship with the company does not reflect its impact on your bottom line. To understand how customer churn affects the companys it is necessary to calculate gross revenue churn (the percentage of revenue that is lost during a targeted period) or employ more complex calculating methods.
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Considerations When Calculating Churn Rate
Sample size
Time frame
Customer segments
Seasonality

sample size
Early on and under conditions of hyper growth, calculated churn rate is just as much a product of our small sample size as it is a number that’s representative or predictive of how well the company’s service retains customers. There is often little data in terms of number of cohorts and how the cohorts behave over time.
Under these conditions, it’s important to recognize the limitations of the inferences you can draw from your churn rate.
Time frame
The company may be looking at customer churn over the period of a week, month, quarter, or year. Also, they want their calculation to be robust with respect to the timeframe chosen. The company should avoid their calculation going from generally correct to wildly incorrect when they move from a monthly frame to a quarterly frame.
Customer segments
An aggregated number dissolves the differences between the company’s customers, and that can lead to a misunderstanding of the churn number if it is just take it at face value. For instance, growth in a higher churn customer segment could be mistaken for increased churn overall, and that could lead down the wrong path of trying to fix a non-existent churn problem.
Seasonality
If the business varies based on the season, the customer’s churn may show changes that correspond with the seasonality of the business that might be hard to understand until they gone through several cycles.
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Appropriate Churn Models
Identification of the best data:
The company must first obtain quality and substantive data for accuracy of results.
Selection of Attributes:
The company’s analyst must select the best attribute from the available ones.

Identification of the best data
Experience shows that the quality and suitability of the available data determines the accuracy and predictive power of the resulting model. Different data combinations may be better or worse indicators for different problems and for different areas of business. Ultimately, it is a question of identifying the data that best fit the type of analysis being carried out. Only in this manner could useful and usable knowledge (in business terms) be extracted in subsequent stages of analysis. (Erl, 2016)
. Selection of Attributes
In this stage, the most appropriate attributes or features for prediction must be selected from those available to the analyst, which, in a supervised PR setting, which is the most common in the literature of churn IDA, would be those that minimize the classification or prediction error; in an unsupervised learning setting, which might address churn analysis as a market segmentation problem, would be those which best reflect the grouping or cluster structure of the data. From a DM process point of view, this stage would correspond to the phase of data pre-processing. (Waller and Fawcett, 2013)
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Identification of the Best Data

Selection of Attributes

Appropriate Churn Models
(Cont.)
Development of a predictable model :
The use of a model that extracts data patterns from the available data.
In this case, regression analysis is the best model to be used by the company.
Results validation:
Results can best be validated using the repeated random sub-sampling technique.

Development of a predictable model
Computational Intelligence (CI) methods provide, in one form or another, flexible information processing capabilities for handling real life problems. Exploiting the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness, low solution cost and close resemblance with human-like decision making, is the overall objective of CI methods.
Results validation
Cross-validation will be the most suitable in this cases in which data is scarce. In its most simple version, a single split of the data is generated (such as the 70/30 used in [46]; the 70% of cases used as training set and the 30% remaining as validation set). Cross-validation is based on the principle of using the available data for both training and validation. (Erl, 2016)
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Regression Analysis

Random Sub-Sampling Technique

Prevention of Client Value Attrition
This analysis involves more than just the prediction of customer churn.
It gives access to prediction of clients with a declining lifetime value
Gives the company an opportunity to increase revenue from existing clients.

Optimove goes beyond simply predicting which customers will abandon the business by providing early warnings regarding customers whose lifetime value prediction has declined substantially during the recent period, even though they are still active and may not abandon the business entirely in the near future. (Erl, 2016)
Optimove’s ability to identify customers which fall into this “decliner” category helps marketers increase revenues from existing customers, while simultaneously reducing the number of customers who may fall into the risk-of-churn category.
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How to Reduce Customer Churn

Analyze causes of churn
Engage with clients
Offer incentives

 Communicating with the customers does miracles in analyzing churn. The company needs to be actively using all channels for that: phone, e-mail, website, live chat, and social media. The valuable feedback on how well they serve their customers is just a phone call, an e-mail or a survey away. Also, another way to prevent churn is to actively engage the customers with the product (Agarwal, 2014). The company should give its customers reasons to keep coming back by showing them the day-to-day value of using their products, by making their products and services a part of their daily workflow. Lastly, offering incentives and discount offers is widely regarded as the most effective tactic in reducing churn.
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Solution to Customer Churn

Acquisition and aggregation of data
Data integration and analysis
Data interpretation

Phase 1: Data Acquisition and Aggregation
The first phase of this customer churn analytics engagement revolved around combining data obtained from disparate sources and customer touch points to identify customers with the highest churn risk.
Phase 2: Data Integration and Analysis 
The second phase focused on integrating and analyzing data using advanced statistical models to generate comprehensive insights that help predict churn & analyze factors leading to customer churn. (Agarwal, 2014)
(Phase 3: Data Interpretation
In the third phase of this customer churn analytics engagement, the company should focus on interpreting data & developing corrective measures to reduce the business risk of existing customers switching to its competitors’ network by implementing innovative ways to improve the overall customer experience
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Conclusion
Yore Blends should identify possibilities of churn before it happens.
Appropriate churn analysis will help the company reduce chances of loosing clients.
The most appropriate analysis technique should be executed.
Failure to identify gaps will lead to decrease in company’s revenue.

References
Agarwal, R., & Dhar, V. (2014). Big data, data science, and analytics: The opportunity and challenge for IS research.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and
Erl, T., Khattak, W., & Buhler, P. (2016). Big data fundamentals: concepts, drivers & techniques. Prentice Hall Press. management. Journal of Business Logistics, 34(2), 77-84.

References

Salloum, S., Dautov, R., Chen, X., Peng, P. X., & Huang, J. Z. (2016). Big data analytics on Apache Spark. International Journal of Data Science and Analytics, 1(3-4), 145-164.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.